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0x698e692B557310fE216127FF9Dd65E06782f6425

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0x25b93ff09948D6E1B40aA0190f5B3bA0B3d65139

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0x25b93ff09948D6E1B40aA0190f5B3bA0B3d65139

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"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

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#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

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*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. author2vec http://dl.acm.org/citation.cfm?id=2889382
5. bb2vec https://arxiv.org/pdf/1809.09621.pdf
6. behavior2vec https://dl.acm.org/citation.cfm?id=3184454
7. care2vec https://arxiv.org/abs/1812.00715
8. cat2vec http://104.155.136.4:3000/forum?id=HyNxRZ9xg

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1) Input MNIST: [28 x 28 x 1] ( 1 color channel)
2) Kernel = [9x9]
3) Apply kernel to input 256 times:
(28 - 9 ) + 1 = 20
Output is 256 x 20 x 20 
4) Relu: 256 x 20 x 20
5) Apply kernel again with stride = 2:
((20 - 9) + 1) = 6
Output is 256 x 6 x 6
6) We’re going to cut the stack up into 32 decks with 8 cards each deck.

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import shutil
import os
import random
from random import shuffle
from shutil import copyfile
from concurrent.futures import ThreadPoolExecutor

# ROOT = "/home/nghibui/codes/bi-tbcnn/"
src_dir = "train"
tgt_dir = "train_val"

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import codecs
import queue  # or queue in Python 3
import threading
import os

class PrintThread(threading.Thread):
    def __init__(self, queue):
        threading.Thread.__init__(self)
        self.queue = queue